With the popularity of smart devices， the rapid development of Internet， social and multimedia software technologies， the accessibility and self-control of images have been greatly improved， and the demand for pornographic， violent and other undesirable image content supervision and intelligent review has rapidly increased. In view of the shortcomings of manual and traditional algorithm image content review and identification in scale， flexibility and response time， and the lack of existing related image data samples， a web image content review model based on deep transfer learning is proposed. According to the self-built violent image sample data set， two typical pre-trained deep neural network models， VGG16 and Resnet50， were selected for transfer learning， and the image content review model was finally optimized through fine-tuning of the weight parameters. Comparative experiments show that the recognition performance of the image content review model is significantly better than other existing methods， and the accuracy rate is more than 95%， which can meet the actual application needs.